The simulations in our scenario analyses show behaviour comparable to Cambodia with parameters consistent with literature and expert elicitation. Many of the model parameters are location-specific such as the bite rate, relative infectiousness of asymptomatic carriers, probability of death from radical cure and the initial model state. In the future, we aim to provide a statistical framework for fitting this model, so that it may be applied in contexts where parameters may differ. The complexity of the multi-species model poses a challenge to jointly fitting all model parameters because of the high dimension of the parameter space and the run time, which was on the scale of minutes. Optimised approximate Bayesian inference methods such as Bayesian Optimization for Likelihood-Free Inference (BOFLI) and Likelihood-Free Inference by Ratio Estimation (LFIRE) may provide solutions to both of these challenges [1]}, [2]}. If the run time of the stochastic model becomes prohibitive for inference, as may be the case when applied to larger populations with high prevalence (where capturing small fluctuations in low numbers is less important), a deterministic or hybrid model equivalent could be applied instead.
